This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE of the following are valid metrics for evaluating a regression model?
R-squared (R²)
R-squared is a common regression metric.
F1 score
Why wrong: F1 score is for classification.
Mean Absolute Error (MAE)
MAE is a standard regression metric.
Root Mean Squared Error (RMSE)
RMSE is a standard regression metric.
Accuracy
Why wrong: Accuracy is for classification.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
R-squared (R²)
R-squared (R²) is a valid regression metric that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, and is commonly used alongside other error metrics to assess model performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
R-squared (R²)
Why this is correct
R-squared is a common regression metric.
Related concept
Read the scenario before looking for a memorised answer.
F1 score
Why it's wrong here
F1 score is for classification.
Mean Absolute Error (MAE)
Why this is correct
MAE is a standard regression metric.
Related concept
Read the scenario before looking for a memorised answer.
Root Mean Squared Error (RMSE)
Why this is correct
RMSE is a standard regression metric.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is for classification.
Common exam traps
The trap here is that candidates confuse classification metrics (F1 score, Accuracy) with regression metrics, especially when the question asks for 'valid metrics' without specifying the model type, leading them to select metrics they are more familiar with from classification tasks.
Detailed technical explanation
R² is calculated as 1 - (SS_res / SS_tot), where SS_res is the sum of squared residuals and SS_tot is the total sum of squares. A subtle behavior is that R² can be artificially inflated by adding more predictors, even if they are irrelevant, which is why adjusted R² is often preferred in multiple regression. In real-world scenarios like predicting house prices, R² helps quantify how well features like square footage and location explain price variance, but it does not capture prediction error magnitude, so it is used alongside MAE or RMSE.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: R-squared (R²) — R-squared (R²) is a valid regression metric that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, and is commonly used alongside other error metrics to assess model performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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